Individualized estimation of the Speech Intelligibility Index for short sentences: Test-retest reliability

Toward parametric Bayesian adaptive procedures for multi-frequency categorical loudness scaling

A series of Bayesian adaptive procedures to estimate loudness growth across a wide frequency range from individual listeners was developed, and these procedures were compared. Simulation experiments were conducted based on multinomial psychometric functions for categorical loudness scaling across ten test frequencies estimated from 61 listeners with normal hearing and 87 listeners with sensorineural hearing loss. Adaptive procedures that optimized the stimulus selection based on the interim estimates of two types of category-boundary models were tested. The first type of model was a phenomenological model of category boundaries adopted from previous research studies, while the other type was a data-driven model derived from a previously collected set of categorical loudness scaling data. An adaptive procedure without Bayesian active learning was also implemented. Results showed that all adaptive procedures provided convergent estimates of the loudness category boundaries and equal-loudness contours between 250 and 8000 Hz. Performing post hoc model fitting, using the data-driven model, on the collected data led to satisfactory accuracies, such that all adaptive procedures tested in the current study, independent of modeling approach and stimulus-selection rules, were able to provide estimates of the equal-loudness-level contours between 20 and 100 phons with root-mean-square errors typically under 6 dB after 100 trials.

Spectral weighting for sentence recognition in steady-state and amplitude-modulated noise

Spectral weights in octave-frequency bands from 0.25 to 4 kHz were estimated for speech-in-noise recognition using two sentence materials (i.e., the IEEE and AzBio sentences). The masking noise was either unmodulated or sinusoidally amplitude-modulated at 8 Hz. The estimated spectral weights did not vary significantly across two test sessions and were similar for the two sentence materials. Amplitude-modulating the masker increased the weight at 2 kHz and decreased the weight at 0.25 kHz, which may support an upward shift in spectral weights for temporally fluctuating maskers.

Updating the Spectral Correlation Index: Integrating Audibility and Band Importance Using Speech Intelligibility Index Weights

The original Spectral Correlation Index (SCI_o ) is a measure of amplitude envelope distortion that has been used in several studies to predict behavioral results. Because the original SCI_o did not account for the differential contribution of particular frequency bands to speech intelligibility (i.e., band importance) or for audibility, a new "individual" version (the SCI_i ) is proposed and evaluated. Sentence intelligibility data are used to compare the predictive power and goodness-of-fit for statistical models using two versions of the SCI. The SCI_i provides significantly better fits to behavioral data than the SCI_o . This result demonstrates the importance of accounting for and including signal audibility in analyzing and modeling data collected from the population of individuals with hearing impairment. With this update, the SCI_i is a useful measure for predicting speech intelligibility based on amplitude envelope distortions.